Similar with self-driving cars, they may work with (made up number) 99% accuracy, but that 1% is still too risky.

Regardless of what the AI says, I would still ask a doctor to see my scan considering a false-negative could cost me my life and a false-positive would probably mean a doctor would double check it anyway.

The bottleneck would still be the person who looks at each scan personally.

That being said, I think there is huge potential in early risk prediction using ML long before a real human could even spot cancer tissue.

It isn’t so easy. I mean, you can make something that works more or less. But moving it to a level where it can really be useful clinically will require a huge effort. Because you need to work at extremely low false positive rates while being very sensitive at detection. And, although cancer can be quite obvious in diagnostic scans, in screening scans they are usually very subtle (because screening is done without any symptoms). It will be like looking for a needle in the haystack. And try doing this in that sensitivity/specificity requirements.

I think in general the problem with engineers working on medical topics is that they underestimate the complexity of the problems and the requirement to have in-depth insights about the problems they work on. I get a similar impression from your posts.